Econometrics Chengyuan Yin School of Mathematics

22. The Generalized Method of Moments Econometrics 22. The Generalized Method of Moments

The Method of Moments

Estimating a Parameter Mean of Poisson p(y)=exp(-λ) λy / y! E[y]= λ. plim (1/N)Σiyi = λ. This is the estimator Mean of Exponential p(y) = λ exp(- λy) E[y] = 1/ λ. plim (1/N)Σiyi = 1/λ

Normal Distribution

Gamma Distribution

The Linear Regression Model

Instrumental Variables

Maximum Likelihood

Behavioral Application

Identification Can the parameters be estimated? Not a sample ‘property’ Assume an infinite sample Is there sufficient information in a sample to reveal consistent estimators of the parameters Can the ‘moment equations’ be solved for the population parameters?

Identification Exactly Identified Case: K population moment equations in K unknown parameters. Our familiar cases, OLS, IV, ML, the MOM estimators Is the counting rule sufficient? What else is needed? Overidentified Case Instrumental Variables The Covariance Structures Model Underidentified Case Multicollinearity Variance parameter in a probit model

Overidentification

Overidentification

Underidentification Multicollinearity: The moment equations are linearly dependent. Insufficient Variation in Observable Quantities P P P Q Q Q The Data Competing Model A Competing Model B Which model is more consistent with the data?

Underidentification – Model/Data

Underidentification - Math

Agenda The Method of Moments. Solving the moment equations Exactly identified cases Overidentified cases Consistency. How do we know the method of moments is consistent? Asymptotic covariance matrix. Consistent vs. Efficient estimation A weighting matrix The minimum distance estimator What is the efficient weighting matrix? Estimating the weighting matrix. The Generalized method of moments estimator - how it is computed. Computing the appropriate asymptotic covariance matrix

Inference Testing hypotheses about the parameters: Wald test A counterpart to the likelihood ratio test Testing the overidentifying restrictions

The Method of Moments Moment Equation: Defines a sample statistic that mimics a population expectation: The population expectation – orthogonality condition: E[ mi () ] = 0. Subscript i indicates it depends on data vector indexed by 'i' (or 't' for a time series setting)

The Method of Moments - Example

Application

Method of Moments Solution create ; y1=y ; y2=log(y)$ calc ; m1=xbr(y1) ; ms=xbr(ys)$ sample ; 1 $ mini ; start = 2.0, .06 ; labels = p,l ; fcn= (l*m1-p)^2 + (m2 - psi(p)+log(l)) ^2 $ +---------------------------------------------+ | User Defined Optimization | | Dependent variable Function | | Number of observations 1 | | Iterations completed 6 | | Log likelihood function .5062979E-13 | +---------+--------------+ |Variable | Coefficient | P 2.41060361 L .07707026

Nonlinear Instrumental Variables There are K parameters,  yi = f(xi,) + i. There exists a set of K instrumental variables, zi such that E[zi i] = 0. The sample counterpart is the moment equation (1/n)i zi i = (1/n)i zi [yi - f(xi,)] = (1/n)i mi () = () = 0. The method of moments estimator is the solution to the moment equation(s). (How the solution is obtained is not always obvious, and varies from problem to problem.)

The MOM Solution

MOM Solution

Variance of the Method of Moments Estimator

Example 1: Gamma Distribution

Example 2: Nonlinear Least Squares

Variance of the Moments

Properties of the MOM Estimator Consistent? The LLN implies that the moments are consistent estimators of their population counterparts (zero) Use the Slutsky theorem to assert consistency of the functions of the moments Asymptotically normal? The moments are sample means. Invoke a central limit theorem. Efficient? Not necessarily Sometimes yes. (Gamma example) Perhaps not. Depends on the model and the available information (and how much of it is used).

Generalizing the Method of Moments Estimator More moments than parameters – the overidentified case Example: Instrumental variable case, M > K instruments

Two Stage Least Squares How to use an “excess” of instrumental variables (1) X is K variables. Some (at least one) of the K variables in X are correlated with ε. (2) Z is M > K variables. Some of the variables in Z are also in X, some are not. None of the variables in Z are correlated with ε. (3) Which K variables to use to compute Z’X and Z’y?

Choosing the Instruments Choose K randomly? Choose the included Xs and the remainder randomly? Use all of them? How? A theorem: (Brundy and Jorgenson, ca. 1972) There is a most efficient way to construct the IV estimator from this subset: (1) For each column (variable) in X, compute the predictions of that variable using all the columns of Z. (2) Linearly regress y on these K predictions. This is two stage least squares

2SLS Algebra

Method of Moments Estimation

FOC for MOM

Computing the Estimator Programming Program No all purpose solution Nonlinear optimization problem – solution varies from setting to setting.

Asymptotic Covariance Matrix

More Efficient Estimation

Minimum Distance Estimation

MDE Estimation: Application

Least Squares

Generalized Least Squares

Minimum Distance Estimation

Optimal Weighting Matrix

GMM Estimation

GMM Estimation

A Practical Problem

Testing Hypotheses

Application: Dynamic Panel Data Model